Systems and methods for improved anterior segment OCT imaging

ABSTRACT

Various methods and systems for improved anterior segment optical coherence tomography (OCT) imaging are described. One example method includes collecting a set of OCT data of the cornea of the eye; segmenting the set of OCT data to identify one or more corneal layers; fitting a two-dimensional model of corneal surfaces to the one or more corneal layers; determining motion-correction parameters by minimizing error between the one or more corneal layers and the two-dimensional model of the corneal surfaces; and creating a motion-corrected corneal image dataset from the set of OCT data using the motion-correction parameters. The motion-corrected corneal image dataset can be used to create a model of the anterior and/or posterior surfaces of the cornea. The model of the cornea is used to generate high density and motion-artifact free epithelial thickness maps, which are used for identifying or quantifying pathology such as keratoconus.

PRIORITY

The present application claims priority to Provisional Application Ser.No. 62/361,651 filed Jul. 13, 2016 and Provisional Application Ser. No.62/384,974 filed Sep. 8, 2016, the contents of both of which are herebyincorporated by reference in their entirety.

FIELD OF THE INVENTION

The present application relates to the field of optical coherencetomography (OCT), and in particular, systems and methods of improved OCTimaging of the cornea.

BACKGROUND

Optical coherence tomography (OCT) is an optical imaging technology forperforming in situ real-time cross-sectional imaging of tissuestructures at a resolution of less than 10 microns. OCT measures thescattering profile of a sample along the OCT beam. Each scatteringprofile is called an axial scan, or A-scan. Cross-sectional images,called B-scans, and by extension 3D volumes, are built up from manyA-scans, with the OCT beam illuminating to a set of transverse locationson the sample either by scanning or field illumination.

It has been demonstrated that Fourier domain OCT (FD-OCT) has advantagesover the original time-domain OCT (TD-OCT) (see for example, R. A.Leitgeb et al. (2003). “Performance of fourier domain vs. time domainoptical coherence tomography.” Optics Express 11(8): 889-94; J. F. deBoer et al. (2003). “Improved signal-to-noise ratio in spectral-domaincompared with time-domain optical coherence tomography.” Optics Letters28(21): 2067-2069; M. A. Choma et al. (2003). “Sensitivity advantage ofswept source and Fourier domain optical coherence tomography.” OpticsExpress 11(18): 2183-89). In TD-OCT, the optical path length between thesample and reference arms needs to be mechanically scanned. In FD-OCT,on the other hand, the optical path length difference between the sampleand reference arm is not mechanically scanned. Instead, a full A-scan isobtained in parallel for all points along the sample axial line within ashort time, determined by the wavelength sweep rate of a swept source inswept-source OCT (SS-OCT) or the line scan rate of the line scan camerain spectral-domain OCT (SD-OCT). As a result, the speed for each axialscan can be substantially increased as compared to the mechanicalscanning speed of TD-OCT.

Even with the increased speed of FD-OCT, however, the accuracy of OCTfor a number of ophthalmic applications can be limited by the effects ofeye motion during data acquisition. These applications includepachymetry (i.e., measurement of corneal thickness), keratometry (i.e.,measurement of the curvature of the anterior surface of the cornea),corneal power calculations, epithelial thickness mapping, and cornealtopography. The quality of the data affects the performance ofalgorithms that generate measurements. These algorithms include cornealmotion correction, corneal layers segmentation, epithelial mapping, etc.Corneal motion correction may be necessary due to poor fixation targets,poor fixators, and longer scan times (e.g., repeat scans to improve thesignal to noise (SNR) and contrast). In some instances, a check of thecorneal scan quality may be desirable prior to performing the cornealmotion correction to avoid time spent processing sub-optimal data. Someof the factors that could lead to poor quality data include large eyemotion in the scan data, inexperienced operator, poor alignment, poorfixation target, and/or poor fixator.

An existing method for correcting the effects of eye motion in cornealscans is described by U.S. Pat. No. 9,101,294, the contents of which arehereby incorporated by reference. This method includes acquiring a firstsparse set of data using an OCT system. This first sparse set of data isacquired in a relatively short amount of time (e.g., within a few tensof milliseconds), which can be realized with an ultrafast system havinga speed greater than 100 kHz. The first sparse set of data is used tocreate an initial surface model of the cornea, which is then used toregister a second set of more dense data acquisition. From this secondset of dense data, a more accurate motion-corrected model of the corneais created.

The motion-corrected model of the cornea can be used to generatemotion-artifact free epithelial thickness maps. An epithelial thicknessmap is used for analyzing the human corneal epithelium thickness, whichcan facilitate in the early stage detection of keratoconus. Keratoconusis a progressive eye disease in which the normally round cornea thinsand begins to bulge into a cone-like shape. This cone shape deflectslight as it enters the eye on its way to the light-sensitive retina,causing distorted vision. Front surface corneal topography is thecurrent standard for keratoconus screening. Epithelial thickness mapscan be used as an additional diagnostic tool to improve early detectionof keratoconus when corneal topography is uncertain on diagnosis. U.S.Publication No. 2013/0128222 describes a method for measuring thecorneal epithelial thickness and generating an epithelial thickness mapfor keratoconus diagnosis.

Here we describe new and improved methods of 1) motion correction incorneal scans and 2) generating epithelial thickness maps, and 3) scanquality assessment in corneal image data of an eye.

SUMMARY

According to one aspect of the subject matter described in the presentapplication, a method of motion correction in corneal image data of aneye using an optical coherence tomography (OCT) system includescollecting a set of OCT data of the cornea of the eye; segmenting theset of OCT data to identify one or more corneal layers; fitting atwo-dimensional model of corneal surfaces to the one or more corneallayers; determining motion-correction parameters by minimizing errorbetween the one or more corneal layers and the two-dimensional model ofthe corneal surfaces; creating a motion-corrected corneal image datasetfrom the set of OCT data using the motion-correction parameters; andstoring or displaying the motion-corrected corneal image dataset orinformation derived from the motion-corrected corneal image dataset.

This method of motion correction is particularly advantageous in anumber of respects. By way of example and not limitation, (1) the methodallows longer scan time (e.g., multiple scans for averaging), (2) it canwork with slower OCT systems (e.g., 27 kHz), (3) no need for anadditional set of scan data (e.g. sparse scan data) or other modalities(e.g., Placido based corneal topography) as a reference for motioncorrection and instead takes advantage of natural shape of cornea to beused as the reference, (4) existing scans can be corrected, and (5)optimization convergence is relatively fast (e.g., less than 5 seconds).

According to another aspect of the subject matter described in thepresent application, a method of analyzing an epithelial layer of acornea of an eye using an optical coherence tomography (OCT) systemincludes collecting a set of B-scans over a range of differenttransverse locations on the cornea of the eye; segmenting each B-scan toidentify an anterior corneal layer and an outer edge of Bowman's layer;calculating thickness values, for each B-scan, by computing the distancefrom the anterior corneal layer to the outer edge of the Bowman's layer;combining the thickness values from the B-scans to create a polarepithelial thickness map; converting the polar epithelial thickness mapto a Cartesian epithelial thickness map using a fitting method; andstoring or displaying the Cartesian epithelial thickness map orinformation derived from the Cartesian epithelial thickness map.

The above method of epithelial thickness mapping is particularlyadvantageous in a number of respects. By way of example and notlimitation, (1) there is no need for repeated B-scans to boost thesignal to noise ratio (SNR) and contrast (e.g. by registration andaveraging), (2) segmentation of outer edge of Bowman's layer is possiblein peripheral region despite weak signals, (3) epithelial thicknessmapping is possible for a larger field of view (e.g. 9-12 mm), (4)motion correction enables 3-D thickness value calculation.

According to yet another aspect of the subject matter described in thepresent application, a method of creating a motion-corrected epithelialthickness map of a cornea of an eye using an optical coherencetomography (OCT) system includes collecting a set of OCT data of thecornea of the eye; segmenting the OCT data to identify one or morecorneal layers; fitting a two-dimensional model of corneal surfaces tothe one or more corneal layers; determining motion-correction parametersby minimizing error between the one or more corneal layers and thetwo-dimensional model of the corneal surfaces; creating amotion-corrected corneal image dataset from the set of OCT data usingthe motion-correction parameters; determining epithelial thickness ofthe cornea from the motion-corrected corneal image dataset; creating anepithelial thickness map based on the determined epithelial thickness ofthe cornea; and storing or displaying the epithelial thickness map or afurther analysis thereof.

According to yet another aspect of the subject matter described in thepresent application, a method to assess the scan quality of cornealimage data of an eye using an optical coherence tomography (OCT) systemincludes collecting a set of OCT data of the cornea of the eye;segmenting the set of OCT data to identify one or more corneal layers;fitting a two-dimensional model of corneal surfaces to the one or morecorneal layers; performing one or more scan quality assessment tests toassess scan quality of collected data based on results of thesegmentation and the fitting; determining whether the one or more scanquality assessment tests meet an acceptable scan quality condition; andre-acquiring the bad data or reporting the results of the determinationto an operator or a further analysis thereof.

This method of scan quality assessment is particularly advantageous in anumber of respects. By way of example and not limitation, the methodreports an informative indicator to an operator if any of the followingsituation occurs (1) poor scan quality (due to blink, partial blink,eyelid/eyelash interference, low contrast, etc.), (2) scan position istoo high or too low, (3) vertex is off center, and (4) large motion(e.g., lateral, rotation motion and tilt in scans). Only when the scanquality is acceptable, the scan data is used for performing subsequentoperations, such as for example, corneal motion correction, epithelialthickness mapping, etc.

Further aspects include various additional features and operationsassociated with the above and following aspects and may further include,but are not limited to corresponding systems, methods, apparatus, andcomputer program products. It should be noted that the above aspects maynot be entirely independent and could be used either alone or incombination with each other.

The features described herein are not all-inclusive and many additionalfeatures will be apparent to one of ordinary skill in the art in view ofthe figures and description. Moreover, it should be noted that thelanguage used in the specification has been principally selected forreadability and instructional purposes and not to limit the scope of theinventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a generalized optical coherence tomography (OCT) system thatcan be used to practice the present invention.

FIG. 2A is a flowchart of an example method for correcting the effectsof eye motion that may occur during data acquisition of a cornea of aneye. FIG. 2B is a flowchart of an example method for assessing scanquality of corneal image data collected using an OCT system. FIGS. 2Cand 2D are flowcharts of a more specific method of scan qualityassessment. FIGS. 2E and 2F shows two exemplary anterior segmentationplots each with a root mean square error (RMSE) beyond a certainthreshold indicating large motion in scan data.

FIG. 3 shows an example of a projection of corneal surface contour linesonto the xy plane and a plot of a meridian and corresponding Zernike fitbefore and after the motion correction.

FIG. 4 shows another example of a projection of corneal surface contourlines onto the xy plane and a plot of a meridian and correspondingZernike fit before and after the motion correction.

FIG. 5 shows a SD-OCT central corneal B-scan and a detailed verticalsection view of human cornea.

FIG. 6 is a flowchart of an example method for generating an epithelialthickness map.

FIG. 7A shows an epithelial thickness map created based on a grid fitmethod. FIG. 7B shows an epithelial thickness map created based on aZernike fitting.

FIGS. 8A and 8B are two examples showing the significance of cornealmotion correction prior to epithelial thickness mapping.

FIG. 9 is a block diagram of an example computer system configured toperform the functions discussed in the present application.

DETAILED DESCRIPTION

All patent and non-patent references cited within this specification areherein incorporated by reference in their entirety to the same extent asif the disclosure of each individual patent and non-patient referencewas specifically and individually indicated to be incorporated byreference in its entirely.

Example OCT System

A generalized FD-OCT system used to collect 3-D image data of the eyesuitable for use with the present invention is illustrated in FIG. 1. AFD-OCT system 100 includes a light source, 101, typical sourcesincluding but not limited to broadband light sources with short temporalcoherence lengths or swept laser sources. A beam of light from source101 is routed, typically by optical fiber 105, to illuminate the sample110, a typical sample being tissues in the human eye. The source 101 canbe either a broadband light source with short temporal coherence lengthin the case of SD-OCT or a wavelength tunable laser source in the caseof SS-OCT. The light is directed towards a region of the sample 110,typically with a scanner 107 between the output of the fiber and thesample, so that the beam of light (dashed line 108) is scanned laterally(in x and y) over the region of the sample to be imaged. Light scatteredfrom the sample is collected, typically into the same fiber 105 used toroute the light for illumination. Reference light derived from the samesource 101 travels a separate path, in this case involving fiber 103 andretro-reflector 104 with an adjustable optical delay. Those skilled inthe art recognize that a transmissive reference path can also be usedand that the adjustable delay could be placed in the sample or referencearm of the interferometer. Collected sample light is combined withreference light, typically in a fiber coupler 102, to form lightinterference in a detector 120. Although a single fiber port is showngoing to the detector, those skilled in the art recognize that variousdesigns of interferometers can be used for balanced or unbalanceddetection of the interference signal. The output from the detector 120is supplied to a processor 121 that converts the observed interferenceinto depth information of the sample. The results can be stored in theprocessor 121 or other storage medium or displayed on display 122. Theprocessing and storing functions may be localized within the OCTinstrument or functions may be performed on an external processing unit(e.g., the computer system 900 shown in FIG. 9) to which the collecteddata is transferred. This unit could be dedicated to data processing orperform other tasks which are quite general and not dedicated to the OCTdevice. The processor 121 may contain for example a field-programmablegate array (FPGA), a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a graphics processing unit (GPU), asystem on chip (SoC) or a combination thereof, that performs some, orthe entire data processing steps, prior to passing on to the hostprocessor or in a parallelized fashion.

The interference causes the intensity of the interfered light to varyacross the spectrum. The Fourier transform of the interference lightreveals the profile of scattering intensities at different path lengths,and therefore scattering as a function of depth (z-direction) in thesample. The profile of scattering as a function of depth is called anaxial scan (A-scan). A set of A-scans measured at neighboring locationsin the sample produces a cross-sectional image (tomogram or B-scan) ofthe sample. A collection of B-scans collected at different transverselocations on the sample makes up a data volume or cube. For a particularvolume of data, the term fast axis refers to the scan direction along asingle B-scan whereas slow axis refers to the axis along which multipleB-scans are collected. A variety of ways to create B-scans are known tothose skilled in the art including but not limited to along thehorizontal or x-direction, along the vertical or y-direction, along thediagonal of x and y, or in a circular or spiral pattern.

The sample and reference arms in the interferometer could consist ofbulk-optics, fiber-optics or hybrid bulk-optic systems and could havedifferent architectures such as Michelson, Mach-Zehnder or common-pathbased designs as would be known by those skilled in the art. Light beamas used herein should be interpreted as any carefully directed lightpath. Instead of mechanically scanning the beam, a field of light canilluminate a one or two-dimensional area of the retina to generate theOCT data (see for example, U.S. Pat. No. 9,332,902; D. Hillmann et al,“Holoscopy—holographic optical coherence tomography” Optics Letters36(13): 2390 2011; Y Nakamura, et al, “High-Speed three dimensionalhuman retinal imaging by line field spectral domain optical coherencetomography” Optics Express 15(12):7103 2007; Blazkiewicz et al,“Signal-to-noise ratio study of full-field Fourier-domain opticalcoherence tomography” Applied Optics 44(36):7722 (2005)). In time-domainsystems, the reference arm needs to have a tunable optical delay togenerate interference. Balanced detection systems are typically used inTD-OCT and SS-OCT systems, while spectrometers are used at the detectionport for SD-OCT systems.

The invention described herein could be applied to any type of OCTsystem. The OCT system could be a stand-alone diagnostic instrument orbe integrated within a surgical microscope such as the ZEISS OPMILUMERA® with RESCAN™.

In FIG. 1, lens (123) is normally called the objective or ocular lens.It is present to produce a focused beam onto a desired part of the eye.In order to accommodate anterior segment (cornea, aqueous humor, andcrystalline lens) and posterior segment (vitreous humor and the variousretinal tissues down to the sclera), the lens (123) needs to have itsfocal length adjusted. There are a variety of ways to achieve this, butoften a method is to insert or add a negative lens at a position justdownstream of its rear vertex (124). Such a lens could be added manuallyby the user and attached to the system via magnets or any otherattachment mechanism known to one skilled in the art. Thus, in thisparticular approach, addition of this lens to the optical configurationof the system permits the instrument to switch between anterior andposterior imaging.

Commercial OCT devices have been developed in the past for imaging boththe anterior and posterior sections of the eye. Some of these are, forexample, Zeiss Cirrus™ HD-OCT, Visante™ Omni, and Stratus™ (Carl ZeissMeditec, Inc. Dublin, Calif.)). The Cirrus™ HD-OCT system allows forimaging both the anterior and posterior regions by inserting a lens tochange the focal properties of the system and adjusting the delay linebetween the sample and reference arms as described in US Publication No.2007/0291277. The Cirrus™ HD-OCT produces images of the anterior segmentof an eye by using spectral domain optical coherence tomography (SD-OCT)technology.

Motion-Correction in Corneal Scans

FIG. 2A shows a method 200 for correcting the effects of eye motion thatmay occur during data acquisition of a cornea of an eye. It should beunderstood that the method 200 described herein is not limited to thesteps and/or operations referenced in this method and that other stepsand/or operations are also possible and are within the scope of thepresent disclosure. It should also be understood that not every stepdescribed herein must be performed.

In block 202, a set of OCT data of the cornea of an eye is collected.The OCT data may comprise a plurality of B-scans acquired over a seriesof transverse locations on the cornea. These B-scans could be of anynumber or shape of scans (e.g., meridional or radial, circular, spiral,etc.). In a preferred embodiment, these B-scans can consist of N sets ofperpendicular meridional B-scans or a cube scan. In some embodiments,the scans are collected with a longer scan time (e.g. denser scans orrepeated B-scans at the same location, which could take anywhere between50 ms and few seconds) and thus are likely affected by the effects ofeye motion including one or both of axial and transverse eye motion.

In block 204, the OCT data (i.e., the B-scans) are segmented to identifyanterior or posterior corneal layers. One effective way of segmentingthe B-scans takes advantage of the fact that the general shape of thecornea can be modeled as a quadric surface. In this approach, initialestimates of the anterior and posterior layers are first identified. Toestimate the initial position of anterior and posterior surface, anormalized cross-correlation is performed between each A-scan and twofunctions. The first function represents the approximate transition fromair to stroma and the second function represents the approximatetransition from stroma to aqueous humor. The positions with the highestnormalized cross-correlation values are recorded as the initial estimateof the anterior or posterior surface. A corneal layer in atwo-dimensional B-scan can be assumed to be a conic section (parabola,ellipse, hyperbola, etc.). A parabola is fitted to the initial estimatedvalues using a robust regression method such as random sample consensus(RANSAC) by robustly estimating the parameters of parabola from the datawhich contains outliers. The final estimates of the layer position canthen be found using a hybrid graph theory and dynamic programmingframework (see for example, S. Timp et al. (2004). “A new 2Dsegmentation method based on dynamic programming applied to computeraided detection in mammography.” Medical Physics 31(5): 958-971). Inthis framework, the parabola fitted to the initial estimated values isused to define a region of interest (ROI) as the region around thisparabola. After an ROI containing a layer (anterior or posterior) isidentified, the graph-based segmentation can be performed within the ROIto obtain the anterior/posterior layer. It should be understood, thesegmentation is not limited to graph-based segmentation and othersegmentation approaches are also possible and are within the scope ofthe present disclosure.

In block 206, a two-dimensional model of the corneal surfaces is thenfitted to the corneal layers identified in block 204. In a preferredembodiment, the surface model can be a quadric (e.g., rotated/tiltedparaboloid, ellipsoid, hyperboloid, sphere, etc.) or a Zernikepolynomial, generally of a lower order. The lower order ensures that themotion correction is possible even in the presence of segmentationerror(s) due to noise and pathology. One method by which the model maybe fitted is a robust fitting method, such as RANSAC fitting. This formsthe initial estimate of the corneal surface. RANSAC assumes that thedata contains data points that are gross errors or outliers, in additionto other data points that are inliers and whose distribution can beexplained by some set of model parameters. As such, it is able to smoothdata sets in which outliers make up a significant portion of the dataset. If a traditional technique for fitting a model, such as leastsquares, was used instead of the robust fitting method, these outlierdata points could lead to inaccurate calculations of lower order ofcorneal surface.

The outliers can be due to, for example, extreme noise, erroneousmeasurements, or incorrect interpretation of the data. In the case ofcorneal imaging, the outliers can also be due to causes such as specularreflection, scarring or pathologies, curvature change after refractivesurgery, blinking during data acquisition, interferences from eyelashesor eyelids, or other artifacts. By using RANSAC, these outliers can bedetected and excluded from the model fit at each iteration.

Next, in block 208, motion correction parameters are determined byminimizing the error between each identified layer and thetwo-dimensional model of the corneal surfaces. In some embodiments,transformations (related to identified corneal layers) that minimize anobjective function (see below) are selected as the motion correctionparameters. The objective function can be a norm of the differencebetween the transformed surface points and the model fit. A possibleadditional term for an objective function could be filter operators(e.g., difference operators) to enforce smoothness since the underlyingcorneal surface is believed to be mostly continuous. One example way ofminimizing the error or the objective function is given as follows:

X = [x₁, x₂, x₃, …  , x_(N)]^(T)  x_(i) = [x_(i), y_(i), z_(i)]^(T)$\min\limits_{T}{{{TX} - {q({TX})}}}^{2}$ l b ≤ T ≤ ubwhere X contains the anterior corneal segmentation points afterdewarping, T is the transformation matrix containing the transformationmatrices for each point, q(.) represents the quadric or a Zernike fit(lower order) to the transformed points at each iteration of theminimization. The transformation parameters can be constrained using alower bound (lb) and an upper bound (up) for each or a group of points(e.g. each meridian).

In some embodiments, the above minimization problem is solved by L-BFGSsolver, which solves smooth, twice differentiable bounded non-linearprograms using a limited memory BFGS Hessian update (see for example, C.Zhu, R. H. Byrd and J. Nocedal, “L-BFGS-B: Algorithm 778: L-BFGS-BFORTRAN Routines for Large Scale Bound Constrained Optimization,” ACMTransactions on Mathematical Software 23(4), pp. 550-560, 2007).

In some embodiments, the most basic motion parameters are the x,y,ztranslation for each meridian scan. However, shear or tilt in eachmeridian scan can be incorporated into the transformation matrix foreach meridian.

In block 210, a motion-corrected corneal image dataset is created usingthe motion correction parameters obtained in block 208, and then inblock 212, the motion-corrected corneal image dataset is stored (e.g.,in data store 914 shown in FIG. 9) or displayed (e.g., via display 122or optional display 910 shown in FIG. 9). In some instances, themotion-corrected dataset is created from the original set of OCT data(collected in block 202) by correcting the lateral and z-displacementeye motion artifacts using the motion correction parameters. Themotion-corrected corneal image dataset can then be used to create amotion-corrected model of the anterior and/or posterior surfaces of thecornea. Modeling the corneal surface is essential for certain anteriorsegment applications. One way of modeling the corneal surface that isparticularly helpful is the use of Zernike polynomials. This type ofpolynomial is good for representing the corneal shape and provides anaccurate solution when the underlying surface is relatively smooth andmotion free. For certain applications, such as keratometry, the 7^(th)order Zernike polynomial that gives 36 Zernike coefficients adequatelyapproximates the corneal surface. In some cases, the coefficients may bedetermined using a robust fitting algorithm, such as Random SampleConsensus (RANSAC) fitting. Once a motion-corrected model of the corneais created, the method 200 can be extended to use the model foradditional applications. These include obtaining highly accurate anddense pachymetry and epithelial thickness maps with minimal motionartifacts, keratometric values, and corneal power measurements.

In some embodiments, the performance of the corneal motion correctionmethod discussed above can be evaluated by calculating the root meansquare (RMS) error between the anterior segmentation data and its 7^(th)order Zernike fit before and after motion correction. FIGS. 3 and 4 showexemplary plots of the projection of corneal surface contour lines (onlydata are plotted with no interpolation) onto the xy plane before andafter motion correction (left and right plots, respectively). Inparticular, FIG. 3 shows, for a scan with small motion, the projectionof the corneal surface contour lines onto the xy plane 302 and the plotof a meridian and corresponding Zernike fit 304 before the motioncorrection, and the projection of the corneal surface contour lines ontothe xy plane 306 and the plot of the same meridian and correspondingZernike fit 308 after the motion correction. As depicted, the projectionof contour lines show circular/elliptical shapes after motioncorrection. The data (blue dashed lines) and the fit (red line) for agiven meridian are shown in the plots 304 and 308. In this particularexample of FIG. 3, the RMS error is 9.16 microns before the motioncorrection and 4.75 microns after the motion correction.

FIG. 4 shows, for a scan with large motion, the projection of thecorneal surface contour lines onto the xy plane 402 and the plot of ameridian and corresponding Zernike fit 404 before the motion correction,and the projection of the corneal surface contour lines onto the xyplane 406 and the plot of the same meridian and corresponding Zernikefit 408 after the motion correction. Here, the RMS error is 48.14microns before the motion correction and 5.66 microns after the motioncorrection.

Scan Quality Assessment in Corneal Image Data

FIG. 2B shows a method 220 for assessing scan quality of corneal imagedata collected using an OCT system. It should be understood that themethod 220 described herein is not limited to the steps and/oroperations referenced in this method and that other steps and/oroperations are also possible and are within the scope of the presentdisclosure. It should also be understood that same reference numeralsare used to refer to the steps discussed above with respect to FIG. 2A,the description for which will not be repeated here.

In step 202, a set of OCT data of the cornea of an eye is collected. TheOCT data as discussed elsewhere herein may comprise a plurality ofB-scans acquired over a series of transverse locations on the cornea. Ina preferred embodiment, these B-scans can consist of N sets ofperpendicular radial B-scans or a cube scan. In some embodiments, thescans are collected with a longer scan time (e.g. denser scans orrepeated B-scans at the same location, which could take anywhere between50 ms and few seconds) and thus are likely affected by eye motionincluding one or both of axial and transverse eye motion.

In step 204, the OCT data (i.e., the B-scans) are segmented to identifyone or more corneal layers based on a segmentation approach discussedabove with respect to FIG. 2A. Next, in step 206, a two-dimensionalmodel of the corneal surfaces is fitted to the one or more corneallayers. In a preferred embodiment, the surface model can be a quadric(e.g., rotated/tilted paraboloid, ellipsoid, hyperboloid, sphere, etc.)or a Zernike polynomial, generally of a lower order. One method by whichthe model may be fitted is a robust fitting method, such as RANSACfitting.

In step 222, one or more scan quality assessment tests are performed toassess scan quality of the collected data based on the results of thesegmentation (step 204) and fitting (step 206). The one or more scanquality assessment tests may include, for example and withoutlimitation, 1) a scan quality test based on confidence values in theresults of the segmentation (steps 252-256 in FIG. 2C), 2) a scanposition test based on z-position of the vertex of each B-scan in theOCT data (steps 258-266 in FIG. 2C), 3) a vertex off center finding testbased on the distance between the vertex position and center of a B-scan(steps 268-272 in FIG. 2D), and 4) a motion test based on a root meansquare (RMS) error between the one or more corneal layers and thetwo-dimensional model of the corneal surfaces (steps 274-278), each ofthese tests is discussed in further detail below with respect to FIGS.2C and 2D.

In step 224, a determination is made as to whether the one or more scanquality assessment tests meet an acceptable scan quality condition. Forinstance, one of the scan quality assessment tests may include computingan error between the one or more corneal layers and the two-dimensionalmodel of the corneal surfaces (e.g., 7^(th) order Zernike polynomial)and then determining whether the error is below a certain threshold(e.g., 60 micron) in order to meet an acceptable scan quality condition.The error between a corneal layer or surface and the two-dimensionalmodel may be large due to 1) blink or partial blink in scan data, 2) lowcontrast at the corneal surfaces, 3) large lateral and axial eye motion,4) low or high scan position, 5) off-centered scans, etc. In someinstances, the error can be computed using an error metric such as, forexample, root mean squared error (RMSE), the sum of square due to error(SSE), R-sqaure, and/or adjusted R-square.

If a scan quality condition is determined to be not satisfied oracceptable in step 224, then its status can be reported to an operator(step 226), discussed in more detail below with respect to FIGS. 2C and2D. An example status may include one or more of a 1) poor scan quality(due to blink, eyelid/eyelash interference, low contrast, etc.), 2)large motion (e.g., lateral, rotational motion and tilt in scans), 3)scan position too high, 4) scan position too low, 5) vertex off center,etc. In response to the indication that the condition is not met, thecurrent collected OCT data may be rejected (step 228) and the method 220may return to automatically collect a new set of corneal image data(step 202) and perform subsequent operations thereon.

If on the other hand, the condition is determined to be satisfied, then,in step 230, motion correction is performed on the collected set ofcorneal image data to create a motion corrected corneal image dataset.In some embodiments, the motion correction step involves determiningmotion correction parameters by minimizing error between the one or morecorneal layers and the two-dimensional model of the corneal surfaces(step 208 of FIG. 2A) and then creating a motion corrected corneal imagedataset using the motion corrected parameters (step 210 of FIG. 2A). Themotion-corrected corneal image dataset may then be stored (e.g., in datastore 914 shown in FIG. 9) or displayed (e.g., via display 122 oroptional display 910 shown in FIG. 9) as discussed elsewhere herein.

FIGS. 2C and 2D show a more specific method 250 of scan qualityassessment. In particular, the method 250 describes a series of scanquality assessment tests or metrics that may be performed to assess thequality of corneal image data at various steps and to report an operatorabout the status on each metric. It should be understood that the method250 is not limited to the scan quality assessments tests/metricsreferenced in this method and that other tests/metrics are also possibleand are within the scope of the present disclosure. Also, it should beunderstood that the same reference numerals are used to refer to thesteps discussed above with respect to FIGS. 2A and 2B, the descriptionfor which will not be repeated here.

After the one or more anterior or posterior corneal layers areidentified by segmenting the OCT data (step 204 of FIGS. 2A and/or 2B),scan quality of the collected data is determined based on confidencevalues in the results of the segmentation (step 252). The confidencevalues may range from −1 to 1 where −1 indicates a low confidence in thesegmentation and 1 indicates a high confidence in the segmentation. Ifany anterior corneal layer has a low confidence value for a given length(e.g., 1000 micron) in any of its peripheral regions, then the scan isconsidered to have a poor quality. As discussed elsewhere herein, thepoor scan quality may be caused by blink, partial blink, eyelid/eyelashinterference, and low contrast, which affects the confidence on thesegmentation specifically in the peripheral regions.

In step 254, if the scan quality is determined to be of poor quality,then a poor scan quality status is reported to an operator (step 256).Otherwise, the method 250 proceeds to step 206 to fit a two-dimensionalmodel of the corneal surfaces to the one or more corneal layersidentified in step 204. For instance, a polynomial second order(γ=α₁χ²+α₂χ+α₃) is fitted to the 3000 micron central region of eachanterior layer or surface using RANSAC robust fit. The center positionof the anterior surface is the origin. In step 258, a value of the fitis determined at the center position of the one or more identifiedlayers and then a decision is made as to whether the value is less thana first threshold (step 260) or more than a second threshold (step 264).The first and the second thresholds are different. If the value of thefit at the center position of any anterior layers of B-scans is lessthan a first threshold (e.g. 50 micron), then the scan is considered tobe too high and a “scan too high” status is reported to the operator(step 262). Otherwise, if the value of the fit at the center position ofany anterior layers of B-scans is greater than a second threshold (e.g.500 micron), then the scan is considered to be too low and a “scan toolow” status is reported to the operator (step 266). The scan being toolow or too high may be caused by axial motion during scan or bymisalignment.

The method 250 then proceeds to step 268 (FIG. 2D) to compute a distancebetween the vertex position and the center or a predefined position ofthe scan. If the distance is greater than a certain threshold (e.g., 500micron) in step 270, then the vertex position is considered to be offcenter and its status is reported to the operator (step 272). Next, instep 274, an error is computed between the one or more corneal layersand the two-dimensional model of the corneal surfaces. For instance, RMSerror (RMSE) between the anterior segmentation and its 7^(th) orderZernike polynomial can be computed to determine amount of motion in thecollected data or scans. If the error is greater than a certain value(e.g., 60 micron) (step 276), then the collected data contains largemotion and cannot be used as an input to the corneal motion correctionalgorithm (e.g., the motion correction module 905 in FIG. 9). By way ofexample illustrations, FIG. 2E shows an anterior segmentation plot witha RMSE of 115 microns indicating large motion. FIG. 2F shows anotheranterior segmentation with a RMSE of 149 microns indicating large motionand blink in the scan data. In step 278, a status indicating largemotion in the collected data is reported to the operator. In response tothe status reported to the operator in steps 256, 262, 266, 272, and/or278, the collected OCT data may be rejected (step 228 in FIG. 2C) andthe method 250 may return to collect a new set of corneal image data(step 202) and perform subsequent operations thereon.

If the result of the decision in step 276 is also negative like theprevious steps 254, 260, 264, and 270, then the collected corneal imagedata is considered to be good and of meeting the desired qualitystandards or metrics. In step 280, the corneal image data can then beused as an input to various algorithms such as the motion correctionmodule 905 (FIG. 9) to perform motion correction (discussed in referenceto FIG. 2A) and/or the thickness map generation module 906 (FIG. 9) toperform epithelial thickness mapping (discussed in reference to FIG. 6).

Epithelial Thickness Maps

In another aspect of the present application, a two-dimensionalepithelial thickness map can be created based on, for example, a radialscan pattern used in the central cornea scan. FIG. 5 shows a SD-OCTcentral corneal B-scan 502 and a detailed vertical section view 504 ofhuman cornea. The thickness map is created based on the anterior and theouter boundary of Bowman's layers. For each radial B-scan, the thicknessis defined as the closest distance from the anterior to the outerboundary of Bowman's layer. Similar to a pachymetry map, the epithelialthickness map is interpolated from polar coordinates into atwo-dimensional Cartesian map.

The invention discussed herein solves a challenging Bowman's layersegmentation problem in the art due to following facts in OCT images:

-   -   Using a single B-scan (not averaged B-scan from multiple B-scans        from the same location)    -   Very narrow intensity difference between corneal epithelium and        stroma.    -   Disconnected Bowman's layer seen within a B-scan.    -   Anterior surface of the stroma is located a few microns below        the Bowman's layer and can be confused with the Bowman's layer.    -   Weak signal at Bowman's layer seen in the peripheral regions.

FIG. 6 shows a method 600 for generating an epithelial thickness mapaccording to the present application. It should be understood that themethod 600 described herein is not limited to the steps and/oroperations referenced in this method and that other steps and/oroperations are also possible and are within the scope of the presentdisclosure.

The method 600 begins by collecting, in block 602, a set of B-scans ofthe cornea of an eye. In some embodiments, the B-scans are collectedover a range of different transverse locations on the cornea such thatno two B-scans are collected at the same transverse location. In someinstances, a B-scan may be optionally downsampled by a factor of 2 inthe lateral direction to produce a smaller B-scan to estimate a regionof interest (ROI). Downsampling reduces the overall execution time.Next, in block 604, each B-scan is segmented to identify an anteriorcorneal layer. The segmentation can be carried out in the same way usinga dynamic programming framework as discussed above with respect to block204 of method 200. The B-scan may be flattened to the anterior layer ina search region of 100 microns. Optionally, average filtering may beperformed in the lateral direction to increase the signal to noise ratio(SNR) and axial gradient to enhance the Bowman's layer edges.

In block 606, an outer edge of the Bowman's layer is identified. Afterthe ROI containing surface of interest (i.e., area around the Bowman'slayer) is identified in block 604, segmentation can be performed withinthe ROI. For example, graph based segmentation can be performed. Thissegmentation works well on a total cost function c. The local costfunctions are derived, for instance, from the image gradient magnitudein A-scan direction. Local cost is the cost assigned to every singlepixel in the ROI. The pixels that most likely belong to the surface willbe assigned low cost and vice versa (see for example, S. Timp, “A new 2Dsegmentation method based on dynamic programming applied to computeraided detection in mammography,” Med. Phys. 31(5): 958-71 (2004)). Inone embodiment, the total cost function c can be computed as follows:

$c = \frac{1}{1 + e^{- \frac{({{{({I*G_{z}})}*G_{x}} - \beta})}{\alpha}}}$

-   -   where ‘*’ stands for convolution.    -   I is the input image.    -   G_(z) is the derivative of a Gaussian function with σ_(z) in the        axial direction.    -   G_(x) is a Gaussian smoothing function with σ_(x) in the lateral        direction, and    -   α and β are sigmoid function parameters.

To find the path (or the edge positions) of the lowest cost whentravelling in the image from left to the right, a cumulative costfunction is constructed as below:

Set the first column of this cumulative cost function to the total(local) cost of these pixels:C(i,0)=c(i,0)

The cumulative cost of the other pixels is calculated recursively:

${C\left( {i,j} \right)} = {\min\limits_{{- m} \leq k \prec m}\left\{ {{C\left( {{i - k},{j - 1}} \right)} + {c\left( {i,j} \right)}} \right\}}$

-   -   where m is the search window in the previous column.

Once the cumulative cost function is established, the optimal path (orthe surface) can be found by back-tracing the path from the last columnto the first column for the lowest cumulative cost.

Next, in block 608, the thickness is calculated for each B-scanindividually. The thickness is measured as the distance from theanterior corneal layer to the outer edge of the Bowman's layer. In someembodiments, this distance is calculated using a distance transformmethod, such as a fast marching method (see for example, Telea, A.(2004). “An Image Inpainting Technique Based on the Fast MarchingMethod.” Journal of Graphics Tools 9(1): 23-34). In block 610, thethickness values of all B-scans are combined to create a polarepithelial thickness map, which is then converted, in block 612, to atwo-dimensional Cartesian map to obtain the epithelial thickness map.This conversion can be based on a grid fit method, Zernike fitting, orother interpolation methods. FIG. 7A shows the epithelial thickness mapcreated based on the grid fit method. FIG. 7B shows the epithelialthickness map created based on the Zernike fitting. These two fittingmethods are individually discussed below:

Grid Fit

Polar to Cartesian conversion is a fitting of the form z(x, y) to polardata. Grid fit can also fit a surface to scattered (or regular) data.The bilinear interpolation at any point inside the grid is a linearcombination of the values at the grid nodes in the locality of the givenpoint. The interpolation problem can be written as a regularizationproblem:

${\min\limits_{z}{\left( {{Az} - b} \right)}^{2}} + {\lambda{{Bz}}^{2}}$

where the vector z is of length m×n; n is the number of grid nodes inthe y direction; m is the number of nodes in the x direction; A is amatrix of k×(m×n), where k is the number of data point; and b is avector of known surface values. B is matrix of k×(m×n) containing thefirst partial derivatives of the surface in neighboring cells. λ is theregularization parameter. If 0<λ<<1, then the surface will be noisy. Thesurface will be smoother for λ>1. (see for example, John R. D'Errico,Understanding Gridfit, Dec. 28, 2006)

Zernike Fitting

Zernike polynomials model and represent the corneal shape. Zernikefitting provides an accurate solution when the underlying surface isrelatively smooth and well-represented by a high order Zernike surface.Zernike fitting smooths the corneal data and provides a low passfiltering effect. The discrete set of data points in the polarcoordinate system are expanded into Zernike polynomials such that

${z\left( {\rho_{i},\theta_{i}} \right)} = {\sum\limits_{n,{\pm m}}^{\;}{a_{n,{\pm m}}{Z_{n}^{\pm m}\left( {\rho_{i},\theta_{i}} \right)}}}$for all points (ρ_(i), θ_(i)). Z_(n) ^(±m)(ρ_(i), θ_(i)) are the Zernikepolynomials.

To transform a polar map to Cartesian coordinates, a Zernike fitprovides an accurate solution. The Zernike fitting could be applied tocorneal surfaces (anterior and posterior) as well as to the corneal mapor the (mean) curvature map for better representation of the surfaces ormaps. Most of the time these maps are sparse and the Zernike fittingrepresents the complete Cartesian representation of these maps fordisplay purposes or further analysis.

In block 614, the generated Cartesian epithelial thickness map or afurther analysis thereof is stored in a data store (e.g., the data store914 shown in FIG. 9) for future reference, access, and/or retrieval, orprovided for display to a user on a display, such as the display (e.g.,the display 122 (FIG. 1) or the optional display 910 (FIG. 9).

Motion-Corrected Epithelial Thickness Maps

In some embodiments, the motion-corrected model of the cornea, discussedabove with respect to method 200, can be used to generatemotion-artifact free epithelial thickness maps. When generating themaps, both the anterior and Bowman layers can be identified using themotion-corrected corneal image dataset as discussed above. In someembodiments, two surfaces can be reconstructed from the anterior and theouter edge of the Bowman's layers using higher order of the Zernikefitting (in the case of redial or sparse scans). Then a completethickness map can be reconstructed by calculating the distance betweenthe two surfaces using a distance transform method. This method givesaccurate pointwise distances between two surfaces in three dimensions.The corneal motion correction is essential for three-dimensionalthickness map reconstruction.

FIGS. 8A and 8B are two examples showing the significance of cornealmotion correction prior to epithelial thickness mapping. The left images802 and 804 represent the epithelial thickness map before motioncorrection, the middle images 806 and 808 represent the map after motioncorrection, and the right images 810 and 812 represent the differencemap between before and after motion correction. A maximum error of +/−2microns has been shown in these two examples which corresponds to 4%error if the average epithelial thickness of normal cases is assumed tobe around 50 microns. This might be significant for refractiveapplications. The error increases as the motion gets larger.

Example Computer System

The processing unit 121 that has been discussed herein in reference toFIG. 1 can be implemented with a computer system configured to performthe functions described for this unit. For instance, the processing unit121 can be implemented with the computer system 900, as shown in FIG. 9.The computer system 900 may include one or more processors 902, one ormore memories 904, a communication unit 908, an optional display 910,one or more input devices 912, and a data store 914. The display 910 isshown with dotted lines to indicate it is an optional component, which,in some instances, may not be a part of the computer system 900. In someembodiments, the display 910 discussed herein is the display 122 thathas been discussed with respect to FIG. 1.

The components 902, 904, 908, 910, 912, and 914 are communicativelycoupled via a communication or system bus 916. The bus 916 can include aconventional communication bus for transferring data between componentsof a computing device or between computing devices. It should beunderstood that the computing system 900 described herein is not limitedto these components and may include various operating systems, sensors,video processing components, input/output ports, user interface devices(e.g., keyboards, pointing devices, displays, microphones, soundreproduction systems, and/or touch screens), additional processors, andother physical configurations.

The processor(s) 902 may execute various hardware and/or software logic,such as software instructions, by performing various input/output,logical, and/or mathematical operations. The processor(s) 902 may havevarious computing architectures to process data signals including, forexample, a complex instruction set computer (CISC) architecture, areduced instruction set computer (RISC) architecture, and/orarchitecture implementing a combination of instruction sets. Theprocessor(s) 902 may be physical and/or virtual, and may include asingle core or plurality of processing units and/or cores. In someembodiments, the processor(s) 902 may be capable of generating andproviding electronic display signals to a display device, such as thedisplay 910, supporting the display of images, capturing andtransmitting images, performing complex tasks including various types offeature extraction and sampling, etc. In some embodiments, theprocessor(s) 902 may be coupled to the memory(ies) 904 via adata/communication bus to access data and instructions therefrom andstore data therein. The bus 916 may couple the processor(s) 902 to theother components of the computer system 900, for example, thememory(ies) 904, the communication unit 908, or the data store 914.

The memory(ies) 904 may store instructions and/or data that may beexecuted by the processor(s) 902. In the depicted embodiment, thememory(ies) 904 stores at least a motion correction module 905, athickness map generation module 906, and a scan quality assessmentmodule 907, each of which may include software, code, logic, or routinesfor performing any and/or all of the functionalities discussed herein.For instance, the motion correction module 905 may perform all or someof the steps of the method 200 depicted in FIG. 2A, the thickness mapgeneration module 906 may perform all or some of the steps of the method600 depicted in FIG. 6, and the scan quality assessment module 907 mayperform all or some of the steps of the methods 220 and 250 depicted inFIGS. 2B-D. In some embodiments, the memory(ies) 904 may also be capableof storing other instructions and data including, for example, anoperating system, hardware drivers, other software applications,databases, etc. The memory(ies) 904 are coupled to the bus 916 forcommunication with the processor(s) 902 and other components of thecomputer system 900. The memory(ies) 904 may include a non-transitorycomputer-usable (e.g., readable, writeable, etc.) medium, which can beany apparatus or device that can contain, store, communicate, propagateor transport instructions, data, computer programs, software, code,routines, etc. for processing by or in connection with the processor(s)902. A non-transitory computer-usable storage medium may include anyand/or all computer-usable storage media. In some embodiments, thememory(ies) 904 may include volatile memory, non-volatile memory, orboth. For example, the memory(ies) 904 may include a dynamic randomaccess memory (DRAM) device, a static random access memory (SRAM)device, flash memory, a hard disk drive, a floppy disk drive, a CD ROMdevice, a DVD ROM device, a DVD RAM device, a DVD RW device, a flashmemory device, or any other mass storage device known for storinginstructions on a more permanent basis.

The computer system for the processing unit 121 may include one or morecomputers or processing units at the same or different locations. Whenat different locations, the computers may be configured to communicatewith one another through a wired and/or wireless network communicationsystem, such as the communication unit 908. The communication unit 908may include network interface devices (I/F) for wired and wirelessconnectivity. For example, the communication unit 908 may include aCAT-type interface, USB interface, or SD interface, transceivers forsending and receiving signals using Wi-Fi™; Bluetooth®, or cellularcommunications for wireless communication, etc. The communication unit908 can link the processor(s) 902 to a computer network that may in turnbe coupled to other processing systems.

The display 910 represents any device equipped to display electronicimages and data as described herein. The display 910 may be any of aconventional display device, monitor or screen, such as an organiclight-emitting diode (OLED) display, a liquid crystal display (LCD). Insome embodiments, the display 910 is a touch-screen display capable ofreceiving input from one or more fingers of a user. For example, thedevice 910 may be a capacitive touch-screen display capable of detectingand interpreting multiple points of contact with the display surface.

The input device(s) 912 are any devices for inputting data on thecomputer system 900. In some embodiments, an input device is atouch-screen display capable of receiving input from one or more fingersof the user. The functionality of the input device(s) 912 and thedisplay 910 may be integrated, and a user of the computer system 900 mayinteract with the system by contacting a surface of the display 910using one or more fingers. In other embodiments, an input device is aseparate peripheral device or combination of devices. For example, theinput device(s) 912 may include a keyboard (e.g., a QWERTY keyboard) anda pointing device (e.g., a mouse or touchpad). The input device(s) 912may also include a microphone, a web camera, or other similar audio orvideo capture devices.

The data store 914 can be an information source capable of storing andproviding access to data. In the depicted embodiment, the data store 914is coupled for communication with the components 902, 904, 908, 910, and912 of the computer system 900 via the bus 916, and coupled, via theprocessor(s) 902, for communication with the motion correction module905, the thickness map generation module 906, and the scan qualityassessment module 907. In some embodiments, each of the motioncorrection module 905, the thickness map generation module 906, and thescan quality assessment module 907 is configured to manipulate, i.e.,store, query, update, and/or delete, data stored in the data store 914using programmatic operations.

In the above description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofthe specification. It should be apparent, however, that the subjectmatter of the present application can be practiced without thesespecific details. It should be understood that the reference in thespecification to “one embodiment”, “some embodiments”, or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin one or more embodiments of the description. The appearances of thephrase “in one embodiment” or “in some embodiments” in various places inthe specification are not necessarily all referring to the sameembodiment(s).

Furthermore, the description can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan contain, store, communicate, propagate, or transport the program foruse by or in connection with the instruction execution system,apparatus, or device.

The foregoing description of the embodiments of the present subjectmatter has been presented for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit the presentembodiment of subject matter to the precise form disclosed. Manymodifications and variations are possible in light of the aboveteaching. It is intended that the scope of the present embodiment ofsubject matter be limited not by this detailed description, but ratherby the claims of this application. As will be understood by thosefamiliar with the art, the present subject matter may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. Furthermore, it should be understood that themodules, routines, features, attributes, methodologies and other aspectsof the present subject matter can be implemented using hardware,firmware, software, or any combination of the three.

The invention claimed is:
 1. A method of motion correction in cornealimage data of an eye using an optical coherence tomography (OCT) system,said method comprising: collecting a first set of OCT data of the corneaof the eye using the OCT system; segmenting the first set of OCT data toidentify a 2D surface distribution of OCT data defining a select one ofone or more corneal layers; iteratively determining motion-correctionparameters by; iteratively fitting a 2D model to the 2D surfacedistribution of OCT data until a minimum error is reached, wherein ineach iteration: a) the 2D surface distribution of OCT data istransformed by the previously calculated motion-correction parameters todefine an interim OCT surface distribution; b) the 2D model is fitted tothe interim OCT surface distribution to define an interim surface model,the 2D surface model not being initialized with parameters from aprevious iteration prior to being fitted to the interim OCT surfacedistribution; c) an interim error between the interim OCT surfacedistribution and the interim surface model is computed; and d) thecurrent motion-correction parameters are updated for the next iterationbased on the computed interim error to approach the minimum error;creating a motion-corrected corneal image dataset from the first set ofOCT data based on the motion-correction parameters; and storing ordisplaying the motion-corrected corneal image dataset or informationderived from the motion-corrected corneal image dataset.
 2. The methodas recited in claim 1, wherein the first set of OCT data consists ofB-scans over a series of transverse locations on the cornea of the eye.3. The method as described in claim 1, wherein the first set of OCT dataincludes one or both of axial and transverse eye motion artifacts. 4.The method as recited in claim 1, wherein the first set of OCT dataconsists of a series of meridional scans.
 5. The method as recited inclaim 1, wherein the model is a quadric surface or a lower order Zernikepolynomial.
 6. The method as recited in claim 1, wherein the fittingstep is based on a robust regression method.
 7. The method as recited inclaim 1 further comprising: using the motion-corrected corneal imagedataset to create a motion-corrected model of the anterior and/orposterior surfaces of the cornea.
 8. The method as recited in claim 7,wherein the motion-corrected model of the anterior and/or posteriorsurfaces of the cornea is used to generate one or more of high densityand motion-artifact free 1) pachymetry maps, 2) epithelial thicknessmaps, 3) keratometric values, 4) corneal power measurements, and 5)corneal topography images.
 9. The method as recited in claim 1, whereinthe error in fitting the 2D model to the 2D surface distribution isminimized based on the norm of the difference.
 10. The method as recitedin claim 1 further comprising: prior to determining the motioncorrection parameters, performing one or more scan quality assessmenttests to assess scan quality of collected OCT data based on results ofthe segmentation and the fitting.
 11. The method as recited in claim 10further including the step of automatically reacquiring a new set of OCTdata of the cornea of the eye or reporting results of the determinationto an operator if the results of the one or more scan quality assessmenttests fail to meet the acceptable scan quality condition.
 12. The methodas recited in claim 10 further comprising: rejecting the OCT data if theresults of the one or more scan quality assessment tests fail to meetthe acceptable scan quality condition.
 13. The method as recited inclaim 10, wherein the one or more scan quality assessment tests includeone or more of 1) a scan quality test based on confidence values in theresults of the segmentation, 2) a scan position test based on z-positionof the vertex of each B-scan in the OCT data, 3) a vertex off centerfinding test based on the distance between the vertex position andcenter or a predefined position of a B-scan, and 4) a motion test basedon a root mean square (RMS) error between the one or more corneal layersand the two-dimensional model of the corneal surfaces.
 14. The method asrecited in claim 10, wherein: performing a scan quality assessment testcomprises computing an error between the one or more corneal layers andthe two-dimensional model of the corneal surfaces; and determiningwhether the results of a scan quality assessment test meet an acceptablescan quality condition comprises determining whether the error is belowa certain threshold.
 15. The method as recited in claim 14, wherein theerror is computed based on one or more error metrics including 1) thesum of squares due to error (SSE), 2) R-square, 3) adjusted R-square,and 4) root mean squared error (RMSE).
 16. The method as recited inclaim 1, wherein: the 2D surface distribution of OCT data defining aselect one of the corneal layers is defined as OCT data, X; the fitted2D model defines a function, q; and the determined motion-correctionparameters define a transformation matrix, T; wherein: the step ofiteratively determining motion-correction parameters includes thedetermination of: ${\min\limits_{T}{{{TX} - {q({TX})}}}^{2}};$ and thestep of creating the motion-corrected corneal image dataset from thefirst set of OCT data is based on the final value of T.
 17. The methodas recited in claim 16, wherein the function, q, is a quadric or Zernikepolynomials.
 18. The method of claim 1, wherein: a plurality of said 2Dsurface distributions of OCT data are identified, each 2D surfacedistribution defining a corresponding one of a plurality of corneallayers; and the step of iteratively determining motion-correctionparameters is applied to the plurality of 2D surface distributions toiteratively fit the plurality of 2D surfaces to corresponding corneallayers.